This study aimed to evaluate the association between exposure to occupational hazards and the metabolic syndrome. A secondary objective was to analyze the additive and multiplicative effects of exposure to risk factors. This retrospective cohort was based on 31,615 health examinees at the Pusan National University Yangsan Hospital in Republic of Korea from 2012–2021. Demographic and behavior-related risk factors were treated as confounding factors, whereas three physical factors, 19 organic solvents and aerosols, and 13 metals and dust were considered occupational risk factors. Time-dependent Cox regression analysis was used to calculate hazard ratios. The risk of metabolic syndrome was significantly higher in night shift workers (hazard ratio = 1.45: 95% confidence interval = 1.36–1.54) and workers who were exposed to noise (1.15:1.07–1.24). Exposure to some other risk factors was also significantly associated with a higher risk of metabolic syndrome. They were dimethylformamide, acetonitrile, trichloroethylene, xylene, styrene, toluene, dichloromethane, copper, antimony, lead, copper, iron, welding fume, and manganese. Among the 28 significant pairs, 19 exhibited both positive additive and multiplicative effects. Exposure to single or combined occupational risk factors may increase the risk of developing metabolic syndrome. Working conditions should be monitored and improved to reduce exposure to occupational hazards and prevent the development of the metabolic syndrome.

This review examines the challenges associated with occupational disease surveillance in Korea, particularly emphasizing the limitations of current data sources such as the Industrial Accident Compensation Insurance (IACI) statistics and special health examinations. The IACI system undercounts cases due to its emphasis on severe diseases and restrictions on approvals. Special health examinations, although they cover a broad workforce, are constrained by their annual scheduling, which leads to missed acute illnesses and subclinical conditions. The paper also explores the history of occupational disease surveillance in Korea, highlighting the fragmented and disease-specific approach of earlier systems. The authors introduce the newly established Korea Occupational Disease Surveillance Center (KODSC), a comprehensive nationwide system designed to gather, analyze, and interpret data on occupational diseases through a network of regional centers. By incorporating hospital-based surveillance and focusing on acute poisonings and other sentinel events, the KODSC aims to overcome the limitations of previous systems and promote collaboration with various agencies. Although it is still in the early stages of implementation, the KODSC demonstrates potential for improving data accuracy and contributing valuable insights for public health policy.

This study aimed to develop prediction models for the work-relatedness of shoulder diseases through machine learning algorithms. The dataset comprised 7,270 cases of 8,302 individuals who applied for occupational diseases and received the final approval decision from the Korea Workers' Compensation and Welfare Service's Disease Evaluation Committee, which is related to shoulder musculoskeletal disorders between January 2020 and December 2021. In this study, demographic analysis and difference of approval rate by shoulder diseases were performed. Additionally, machine learning algorithms, including logistic regression, support vector machine, decision tree, random forest, and the XGBoost, were utilized to construct prediction models for work-relatedness assessment. The performance of each model was evaluated. XGBoost showed an accuracy of 81.64% and an area under the curve of 0.73, and random forest showed an accuracy of 84.46% and an area under the curve of 0.73. Key factors influencing work-relatedness assessment were employment period, physical burden score, gender, and age. The application of various machine learning techniques showed high performance score, representing that it would be helpful to reduce the differences in judgment between occupational environment medicine physicians.

Park S, et al. Ann Occup Environ Med. 2018 Dec;30(1):65. https://doi.org/10.1186/s40557-018-0277-1

Lee E, et al. Ann Occup Environ Med. 2020 Sep;32:e32. https://doi.org/10.35371/aoem.2020.32.e32

Background and objectives: Musculoskeletal disorders (MSDs) account for more than 60% of compensated occupational diseases in Korea. Despite this burden, benchmarks of standardized ergonomic exposure and evidence on the combined effects of risk factors remain limited. This study aimed to construct a body part-specific ergonomic job exposure matrix (JEM) and evaluate the independent and interactive effects of ergonomic, demographic, and work-related factors. Materials and Methods: We analyzed the data of 210,500 workers from the 2nd–6th Korean Working Conditions Survey (2009–2020). A JEM for arms/neck, back, and legs was developed and validated (κ ≥ 0.79). Logistic regression models estimated adjusted odds ratios (aORs), and additive interactions were assessed using relative excess risk due to interaction (RERI), attributable proportion (AP), and the synergy index (SI). Results: High ergonomic exposure was strongly associated with MSDs across all body regions (aORs 2.3–2.5). Age > 45 years, long working hours (>52 h), and high job strain also increased risks (aORs 1.4–2.3). On the additive scale, ergonomic risk combined with older age showed consistent synergy (RERI up to 1.5; SI >1.5), whereas combinations with long working hours or job strain showed attenuation (RERI < 0; SI < 1). Women reported higher crude prevalence but lower adjusted odds (aOR ≈ 0.9). Conclusions: This nationally representative study demonstrates that ergonomic risk, age, long working hours, and job strain are major determinants of MSDs. The validated Korean JEM provides a standardized tool for surveillance and compensation. However, the cross-sectional design limits causal inference. Future longitudinal research with objective exposure measures is needed to strengthen causal inference and guide tailored prevention.